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Porter

Porter

Internal developer platform that deploys and scales apps on your own AWS, GCP, or Azure account.

softwareDevOps & CI/CDinternal developer platformkubernetes deploymentdevops automation

What is Porter?

Porter is a Platform-as-a-Service that abstracts away Kubernetes and cloud infrastructure complexity, letting teams run and scale applications directly inside their own AWS, GCP, or Azure account. It connects to a Git repository for one-click deployment and handles cluster provisioning, networking, load balancing, autoscaling, monitoring, and logging behind the scenes.

SpecificationsAI-estimated

deploymentCloud/SaaS
open source❌ No
api available✅ Yes

Key Features of Porter

Connects to a Git repository and deploys applications automatically without manual Kubernetes configuration.
Runs on the customer's own AWS, GCP, or Azure account rather than a shared multi-tenant cloud.
Automates cluster provisioning, networking, VPC configuration, and load balancing behind the scenes.
Generates preview environments and supports zero-downtime deployments via CI/CD automation.
Uses automatic workload bin-packing via Karpenter to reduce cloud overprovisioning and cost.
Supports GPU instance deployment for ML training and inference workloads.
Offers SOC 2 and HIPAA compliance options for regulated workloads.
Charges based on actual resource consumption rather than flat platform fees.

Use Cases for Porter

1

Migrating off Heroku/Render

Teams moving from simpler PaaS platforms adopt Porter for more control and lower cost at scale.

2

AI/ML infrastructure

AI startups deploy GPU-backed training and inference workloads without managing Kubernetes directly.

3

Startup infrastructure scaling

Fast-growing startups use Porter to scale apps on their own cloud account as usage grows.

4

Regulated workloads

Companies needing SOC 2 or HIPAA compliance deploy through Porter's certified infrastructure.

Pros & Cons of Porter

Pros

  • Runs inside the customer's own cloud account for full control and no vendor lock-in on infrastructure
  • Removes the need for a dedicated in-house Kubernetes/DevOps team
  • Cost-optimized via automatic bin-packing and usage-based pricing
  • Supports GPU workloads for AI/ML teams

Cons

  • Pricing excludes the underlying cloud provider bill, so total cost requires two calculations
  • Requires connecting a real cloud account, adding setup overhead versus fully-hosted PaaS options
  • Enterprise features like SSO and on-prem installs are gated behind custom pricing

Frequently Asked Questions

Does Porter host my app on its own servers?

No, Porter deploys and manages apps inside your own AWS, GCP, or Azure account.

How is Porter priced?

Standard usage is metered at $6/month per GB RAM and $13/month per vCPU, on top of your cloud provider's own charges.

Do I need Kubernetes expertise to use Porter?

No, Porter is designed to abstract away Kubernetes complexity so teams can deploy without deep cluster expertise.

Does Porter support GPU workloads?

Yes, it supports deploying GPU instances for machine learning training and inference.

Pricing Overview

View full pricing →
Paid (Subscription)

Standard

$6/mo per GB RAM, $13/mo per vCPU

Enterprise

Custom

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About the Product

Unclaimed Listing
Platforms
Target AudienceStartups and engineering teams wanting Kubernetes power without the operational overhead

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Tags

internal developer platformkubernetes deploymentdevops automationcloud infrastructurepaas

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